Published on 3 May 2022 by Pritha Bhandari. Revised on 10 October 2022.
Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors.
In other words, can you reasonably draw a causal link between your treatment and the response in an experiment?
Internal validity makes the conclusions of a causal relationship credible and trustworthy. Without high internal validity, an experiment cannot demonstrate a causal link between two variables.
Example: Experiment You want to test the hypothesis that drinking a cup of coffee improves memory. You schedule an equal number of university-aged participants for morning and evening sessions at the laboratory. For convenience, you assign all morning session participants to the treatment group and all evening session participants to the control group.
Once they arrive at the laboratory, the treatment group participants are given a cup of coffee to drink, while control group participants are given water. You also give both groups memory tests. After analysing the results, you find that the treatment group performed better than the control group on the memory test.
Can you conclude that drinking a cup of coffee improves memory performance?
For your conclusion to be valid, you need to be able to rule out other explanations for the results.
There are three necessary conditions for internal validity. All three conditions must occur to experimentally establish causality between an independent variable A (your treatment variable) and dependent variable B (your response variable).
In the research example above, only two out of the three conditions have been met.
Because you assigned participants to groups based on the schedule, the groups were different at the start of the study. Any differences in memory performance may be due to a difference in the time of day. Therefore, you cannot say for certain whether the time of day or drinking a cup of coffee improved memory performance.
That means your study has low internal validity, and you cannot deduce a causal relationship between drinking coffee and memory performance.
External validity is the extent to which you can generalise the findings of a study to other measures, settings or groups. In other words, can you apply the findings of your study to a broader context?
There is an inherent trade-off between internal and external validity; the more you control extraneous factors in your study, the less you can generalise your findings to a broader context.
Example: Internal vs external validity In your study of coffee and memory, the external validity depends on the selection of the memory test, the participant inclusion criteria, and the laboratory setting. For example, restricting your participants to university-aged people enhances internal validity at the expense of external validity – the findings of the study may only be generalisable to university-aged populations.
Threats to internal validity are important to recognise and counter in a research design for a robust study. Different threats can apply to single-group and multi-group studies.
Example: Single-group study A research team wants to study whether having indoor plants on office desks boosts the productivity of IT employees from a company. The researchers give each of the participating IT employees a plant to place by their desktop for the month-long study. All participants complete a timed productivity task before (pretest) and after the study (posttest).
| Threat | Meaning | Example |
|---|---|---|
| History | An unrelated event influences the outcomes. | A week before the end of the study, all employees are told that there will be layoffs. The participants are stressed on the date of the posttest, and performance may suffer. |
| Maturation | The outcomes of the study vary as a natural result of time. | Most participants are new to the job at the time of the pretest. A month later, their productivity has improved as a result of time spent working in the position. |
| Instrumentation | Different measures are used in pretest and posttest phases. | In the pretest, productivity was measured for 15 minutes, while the posttest was over 30 minutes long. |
| Testing | The pretest influences the outcomes of the posttest. | Participants showed higher productivity at the end of the study because the same test was administered. Due to familiarity, or awareness of the study’s purpose, many participants achieved high results. |
Altering the experimental design can counter several threats to internal validity in single-group studies.
Example: Multi-group study A researcher wants to compare whether a phone-based app or traditional flashcards are better for learning vocabulary in a second language. They divide one class from one school into three groups based on baseline (pretest) scores on vocabulary. For 15 minutes a day, Group A uses the phone-based app, Group B uses flashcards, and Group C spends the time reading as a control. Three months later, posttest measures of vocabulary are taken.
| Threat | Meaning | Example |
|---|---|---|
| Selection bias | Groups are not comparable at the beginning of the study. | Low scorers were placed in Group A, while high scorers were placed in Group B. Because there are already systematic differences between the groups at the baseline, any improvements in group scores may be due to reasons other than the treatment. |
| Regression to the mean | There is a statistical tendency for people who score extremely low or high on a test to score closer to the middle the next time. | Because participants are placed into groups based on their initial scores, it’s hard to say whether the outcomes are due to the treatment or statistical norms. |
| Social interaction | Participants from different groups may compare notes and either figure out the aim of the study or feel resentful of others. | Groups B and C may resent Group A because of the access to a phone during class. As such, they could be demoralised and perform poorly. |
| Attrition | Dropout from participants | 20% of participants provided unusable data. Almost all of them were from Group C. As a result, it’s hard to compare the two treatment groups to a control group. |
Altering the experimental design can counter several threats to internal validity in multi-group studies.
Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors.
There are eight threats to internal validity: history, maturation, instrumentation, testing, selection bias, regression to the mean, social interaction, and attrition.
Internal validity is the degree of confidence that the causal relationship you are testing is not influenced by other factors or variables.
External validity is the extent to which your results can be generalised to other contexts.
The validity of your experiment depends on your experimental design.
Attrition bias is a threat to internal validity. In experiments, differential rates of attrition between treatment and control groups can skew results.
This bias can affect the relationship between your independent and dependent variables. It can make variables appear to be correlated when they are not, or vice versa.
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